Wicked Problems and Professional Work: Disrupting Work in a Mature Field with Incumbent Professions
Bibliographic record
Abstract
The emerging institutional work perspective implicates agency and action in institutional dynamics in contrast to the traditional organizational institutional approach. In this dissertation, my objective was to explore institutional disrupting work in a mature field with incumbent professions. I developed a two-part case study of the Alberta addictions treatment field. The first part of the case study explored disrupting work at the field level, the second part at the level of professional practice. Data collection included interviews, observations, and documents. My findings show that at the field level and at the level of practice actors’ actions were disrupting to institutionalized arrangements and practices of professional work. At the field level, I identified three forms of disrupting work: complexifying work, boundary work, and temporal work. At the practice level, I also identified three forms of disrupting work: configuring work, adapting work, and boundary work. I developed three models of disrupting a field level model, a practice level model, and a multi-level model. My research sheds light on disrupting and how disrupting interrupts the institutionalized arrangements and practices of incumbent professions, both of which, despite scholars’ interest in action and agency in institutional life, remain overlooked in empirical research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.042 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".